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DID-EC-AIPW method

Source: R/did_ec_aipw.R
did_ec_aipw.Rd

Creates a method object for difference-in-differences augmented inverse probability weighting (DID-EC-AIPW) estimation with external control borrowing for the open-label extension phase (Zhou et al., 2024, Eq. 5). Doubly robust: consistent if either the propensity score model or the outcome model is correct.

Usage

did_ec_aipw(
  ps_formula,
  trt_formula = NULL,
  outcome_formula,
  bootstrap = 500L,
  bootstrap_ci_type = NULL
)

Arguments

ps_formula

Formula string for the propensity score model predicting trial participation. The right-hand side should use only columns in covariates_col_name. . or any other column is an error; an outcome gives a warning, because adjusting for an outcome measured after randomization can bias the treatment effect.

trt_formula

Formula string for the treatment assignment model, or NULL (default) for the marginal probability of treatment in the trial. The model is fit on trial patients only, and its predicted probabilities set the inverse-probability weights of trial treated and trial control patients. The left-hand side is replaced by the treatment column, so it can be any name. The right-hand side should use only columns in covariates_col_name. . or any other column is an error; an outcome gives a warning, because adjusting for an outcome measured after randomization can bias the treatment effect.

outcome_formula

Character vector of outcome model formulas, one per outcome. Each formula is matched to an outcome by its left-hand side, so the order does not matter. The left-hand side must be the outcome name itself; to model a transformed outcome, transform the column first. The right-hand side should use only columns in covariates_col_name. . or any other column is an error; an outcome gives a warning, because adjusting for an outcome measured after randomization can bias the treatment effect.

bootstrap

Number of bootstrap replicates (at least 2). Defaults to 500. Use about 1000 or more for reported intervals; small values are for quick checks only, and their interval can exclude the point estimate.

bootstrap_ci_type

Bootstrap CI type: one of "perc" (default), "bca", "norm", or "basic". "bca" is slow when bootstrap is smaller than the number of patients: boot::boot.ci() then refits the estimator once for each patient.

Value

An S4 object of class did_ec_aipw_method.

References

Zhou et al. (2024). Estimating treatment effect in randomized trial after control to treatment crossover using external controls. Journal of Biopharmaceutical Statistics. doi:10.1080/10543406.2024.2330209

Examples

did_ec_aipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  trt_formula = "A ~ x1 + x2 + x3 + x4 + x5",
  outcome_formula = c(
    "y1 ~ x1 + x2 + x3 + x4 + x5",
    "y2 ~ x1 + x2 + x3 + x4 + x5",
    "y3 ~ x1 + x2 + x3 + x4 + x5",
    "y4 ~ x1 + x2 + x3 + x4 + x5"
  ),
  bootstrap = 500
)
#> An object of class "did_ec_aipw_method"
#> Slot "ps_formula":
#> [1] "S ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "trt_formula":
#> [1] "A ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "outcome_formula":
#> [1] "y1 ~ x1 + x2 + x3 + x4 + x5" "y2 ~ x1 + x2 + x3 + x4 + x5"
#> [3] "y3 ~ x1 + x2 + x3 + x4 + x5" "y4 ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "method_name":
#> [1] "DID-EC-AIPW"
#> 
#> Slot "bootstrap":
#> [1] 500
#> 
#> Slot "bootstrap_ci_type":
#> [1] "perc"
#>